Processes and apparatus for extracting shapes from images for recognizing objects are used in fields such as object location by robots, recognition of internal organs in medical images and form recognition machines. In computer or machine vision processing, the images being processed are in the form of two dimensional digitized arrays of points or pixels having quantitized intensity levels.
Discontinuity in intensity levels within an image are indications of object boundaries or edges. Detecting the discontinuities in intensity provides for the edge detection of an object in the image. Localized edge detection is frequently the first step in the identification of a shape, often proceeding without a preconceived notion of the shape's pattern. Edge identification is followed by a variety of analysis procedures. The analysis is often hampered by introduction of noise and the loss of information during the edge detection step.
Some approaches to edge identification are based on interpretations of the image coordinate system as a complex plane. This allows the representation of discrete contours as complex periodic functions that lead naturally to the use of quadrature detection. These approaches have thus far been limited to parametrically defined boundaries and not pixel values in an actual image.
The general approach of breaking down the task of shape recognition into sequential pieces without feedback reflects present limitations of computer power, as well as conceptual models. By comparison, biological visual systems in primates, although divided into many levels of processing, are nonetheless integrated in a sense that information flows in both the antrograde and retrograde directions at almost every level. Studies of eye movements show, for example, a tight feedback loop involving the control of eye movements by higher visual processes during visual recognition tasks.